BalancingLearner: test the "cycle" strategy

Author: basnijholt

adaptive/tests/test_balancing_learner.py

@@ -6,6 +6,9 @@
 from adaptive.runner import simple
 
 
+strategies = ["loss", "loss_improvements", "npoints", "cycle"]
+
+
 def test_balancing_learner_loss_cache():
     learner = Learner1D(lambda x: x, bounds=(-1, 1))
     learner.tell(-1, -1)
@@ -26,7 +29,7 @@ def test_balancing_learner_loss_cache():
     assert bl.loss(real=True) == real_loss
 
 
-@pytest.mark.parametrize("strategy", ["loss", "loss_improvements", "npoints"])
+@pytest.mark.parametrize("strategy", strategies)
 def test_distribute_first_points_over_learners(strategy):
     for initial_points in [0, 3]:
         learners = [Learner1D(lambda x: x, bounds=(-1, 1)) for i in range(10)]
@@ -41,7 +44,7 @@ def test_distribute_first_points_over_learners(strategy):
         assert len(set(i_learner)) == len(learners)
 
 
-@pytest.mark.parametrize("strategy", ["loss", "loss_improvements", "npoints"])
+@pytest.mark.parametrize("strategy", strategies)
 def test_ask_0(strategy):
     learners = [Learner1D(lambda x: x, bounds=(-1, 1)) for i in range(10)]
     learner = BalancingLearner(learners, strategy=strategy)
@@ -55,6 +58,7 @@ def test_ask_0(strategy):
         ("loss", lambda l: l.loss() < 0.1),
         ("loss_improvements", lambda l: l.loss() < 0.1),
         ("npoints", lambda bl: all(l.npoints > 10 for l in bl.learners)),
+        ("cycle", lambda l: l.loss() < 0.1),
     ],
 )
 def test_strategies(strategy, goal):